Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

Uniform parallel-machine scheduling to minimize makespan with position-based learning curves

Scheduling with learning effects has become a popular topic in the past decade; however, most of the research focuses on single-machine problems. In many situations, there are machines in parallel and the skills of workers might be different due to their individual experience. In this paper, we study a uniform parallel machine problem in which the objective is to jointly find an optimal assignment of operators to machines and an optimal schedule to minimize the makespan. Two heuristic algorithms are proposed and computational experiments are conducted to evaluate their performance.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Microstructural development and electrical properties of lead zirconate titanate composite films

Dihydroxyalcohols (DIOL route) and 2-methoxyethanol (MOE route) are two of the most commonly used solvents in preparing sol-gel lead zirconate titanate (PZT) films. The employment of the PT(MOE) film as a buffer layer for PZT(DIOL) films not only offers advantages of more precise film thickness and better adhesion to substrate, but also reduces the crystallization temperature, improves the surface smoothness, and increases the grain size of the PZT(DIOL)/PT(MOE) composite films. The PT (MOE) buffer layer also improves electrical properties, such as polarization-electric field behavior, fatigue endurance, and leakage behavior, of PZT films.     

Multitemporal SAR images for monitoring cultivation systems using case-based reasoning

This paper demonstrates that multitemporal satellite SAR images are most suitable for monitoring the rapid changes of cultivation systems in a subtropical region. A new method is proposed by applying case-based reasoning (CBR) techniques to the classification of SAR images. Stratified sampling is carried out to collect the cases so that the variations of backscatters within a class can be appropriately captured. The use of discrete cases can conveniently represent the internal changes of a class under complicated situations, such as spatial changes in soil conditions and terrain features. These spatial variations are difficult to represent by using rules or mathematical equations. The proposed method has better classification performance than supervised classification methods in the study area. The case library is reusable for time-independent classification when the SAR images are acquired at the same time of the crop growth cycles for different years. The proposed method has been tested in the Pearl River Delta in South China.     

Seismic Retrofit of Hollow Rectangular Bridge Columns

The seismic performance of rectangular hollow bridge columns is a significant issue of the high-speed rail project in Taiwan. The flexural ductility and shear capacity of such columns with the configuration of lateral reinforcement used in Taiwan have been studied recently. This paper reports that hollow rectangular bridge columns retrofitted with fiber-reinforced polymer (FRP) sheets were tested under a constant axial load and a cyclically reversed horizontal load to investigate their seismic behavior, including flexural ductility, dissipated energy, and shear capacity. An analytical model was also developed to predict the moment-curvature curve of sections and the load-displacement relationship of columns. Based on the test results, the seismic behavior of such columns will be presented. The test results were also compared to the proposed analytical model. It was found that the ductility factors of the tested piers are in the range from 3.4 to 6.3, and the proposed analytical model can predict the load-displacement relationship of such columns with acceptable accuracy. All in all, FRP sheets can effectively improve both the ductility factor and shear capacity of hollow rectangular bridge columns.     

A developed competitive immunoassay based on impedance measurements for methamphetamine detection

In this study, an electro-microchip was successfully used to detect the impedance signals of various methamphetamine (MET) concentrations based on the developed competitive immunoassay method. MET is a commonly used drug often abused by drug addicts and can cause irregular behavior; therefore, MET concentration detection is important for quantitative analysis. In this study, gold nanoparticles (AuNPs) were introduced into an electro-microchip through the specific binding of antibodies, thus constructing a bridge between two electrodes and allowing electrons to move between them. The decreasing impedance value can be easily measured with a commercial LCR meter. According to the collected measurements, a significant difference was observed in impedance signals after 13?min when MET concentrations were reduced. Additionally, a clear, distinguished impedance (a steep slope for both impedance and MET concentration) in the frequency effect (100?Hz?C1?MHz) was observed at 100?Hz. When the concentration of the anti-MET antibody?Ccolloidal gold conjugates was diluted 100×, the detectable limit for MET concentration was 1?ng/mL with 0.5???g/mL of BSA?CMET conjugate. Therefore, the developed electro-microchip is advantageous because it is effective with small sample volumes (30???L), is a form of rapid quantitative measurement, and works with smaller detectable concentrations than other existing commercial detection products which have the best limit of detection of 100?ng/mL.     

可持续消费的灵魂拷问

对于可持续时尚的理解不仅反映了消费端的价值观,也引导着品牌发展的方向。2020年突如其来的疫情,让人们产生了更多思考。不再“报复式消费”,让可持续和经济繁荣共存,成为人们更加关注的焦点。